RRN is one of the most powerful and robust state-of-the-art algorithms derived from FFNN for modeling sequence data [1]}. RNN differs from other algorithms due to its internal memory and ability to process the sequence data that lacks in other algorithms. Furthermore, RNN trains the model based on the current input and previous learning experience, offering more precise predictions. Therefore, RNNs have promising applications in speech recognition, text summarization, prediction problems, face detection, music composition, and language processing [2]}. Fig. REF  shows a simple RNN architecture consisting of an input layer, one or more hidden layers, and an output layer.
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